Nanostructured metals with designable and controllable structure have received increasing attention in surface enhance Raman scattering (SERS) due to single molecular detection limmit. While great challenge remains in creating large scale substrate with high-density "hotspots" to provide uniform and stable enhancement of Raman signals. Here, we fabricated copper island thin film over eighty square centimeter-scale substrate with tunable particle sizes by combining sputtering with dealloying processes. https://www.selleckchem.com/products/gcn2ib.html The island size can be tailored from 150 nm to 370 nm by controlling parameters and etching conditions, and with optimized surface morphology structure. The detection limit of Crystal violet (CV) molecule reached 0.1 pM. Meanwhile, the copper island thin film presents good homogeneity and stability. Our method is promising to repeatedly fabricate novel metal SERS substrates in large scale with standard properties for sensing applications. © 2020 IOP Publishing Ltd.The rapid emergence of new measurement instruments and methods requires personnel and researchers of different disciplines to know the correct statistical methods to utilize to compare their performance with reference ones and properly interpret findings. We discuss the often-made mistake of applying the inappropriate correlation and regression statistical approaches to compare methods and then explain the concepts of agreement and reliability. Then, we introduce the intraclass correlation as a measure of inter-rater reliability, and the Bland-Altman plot as a measure of agreement, and we provide formulae to calculate them, along with illustrative examples, for different types of study designs, specifically, single measurement per subject, repeated measurement while the true value is constant, and repeated measurement when the true value is not constant. We emphasize the requirement to validate the assumptions of these statistical approaches, and also how to deal with violations and provide formulae on how to calculate the confidence interval for estimated values of agreement and intraclass correlation. Finally, we explain how to interpret and report the findings of these statistical analyses. © 2020 Institute of Physics and Engineering in Medicine.Although three-dimensional (3D) bioprinting technology is rapidly developing, the design strategies for biocompatible 3D-printable bioinks remain a challenge. In this study, we developed a machine learning-based method to design 3D-printable bioink using a model system with naturally derived biomaterials. First, we demonstrated that atelocollagen (AC) has desirable physical properties for printing compared to native collagen (NC). AC gel exhibited weakly elastic and temperature-responsive reversible behavior forming a soft cream-like structure with low yield stress, whereas NC gel showed highly crosslinked and temperature-responsive irreversible behavior resulting in brittleness and high yield stress. Next, we discovered a universal relationship between the mechanical properties of ink and printability that is supported by machine learning a high elastic modulus improves shape fidelity and extrusion is possible below the critical yield stress; this is supported by machine learning. Based on this relationship, we derived various formulations of naturally derived bioinks that provide high shape fidelity using multiple regression analysis. Finally, we produced a 3D construct of a cell-laden hydrogel with a framework of high shape fidelity bioink, confirming that cells are highly viable and proliferative in the 3D constructs. © 2020 IOP Publishing Ltd.Transition metal oxides have attracted lots of interest in lithium ion battery (LIB) due to the high theoretical capacity, however, the large specific volume change, low electrical conductivity and slow intrinsic lithiation/delithiation still limit the practical applications. In order to overcome the challenge, a novel type of high temperature annealing treatment for the synthesis of 3D porous FeOx nanocrystals embedded in a partially carbon matrix as an example for high-performance LIB is reported. The FeOx/carbon nanocomposites with coral-like architecture and 71.3 wt% Fe3O4 achieved at 700 ℃ (F700) exhibit good long term cyclability with a reversible capacity 1,012 mAh g-1 remain after 500 cycles at 1.0 A g-1 and the high rate capacity with a reversible capacity of 233 mAh g-1 even at extremely high current density of 20 A g-1. These excellent electrochemical performances could be attributed to the 3D porous structure and carbon coating, which could not only provide excellent electronic conductivity and enough elastic buffer space to accommodate volume changes upon lithium insertion/extraction, but also effectively avoid agglomeration of the Fe3O4 nanocrystals and maintain the structural integrity of the electrode during the charge/discharge process. © 2020 IOP Publishing Ltd.Skin lesion datasets consist predominantly of normal samples with only a small percentage of abnormal ones, giving rise to the class imbalance problem. Also, skin lesion images are largely similar in overall appearance owing to the low inter-class variability. In this paper, we propose a two-stage framework for automatic classification of skin lesion images using adversarial training and transfer learning toward melanoma detection. In the first stage, we leverage the inter-class variation of the data distribution for the task of conditional image synthesis by learning the inter-class mapping and synthesizing under-represented class samples from the over-represented ones using unpaired image-to-image translation. In the second stage, we train a deep convolutional neural network for skin lesion classification using the original training set combined with the newly synthesized under-represented class samples. The training of this classifier is carried out by minimizing the focal loss function, which assists the model in learning from hard examples, while down-weighting the easy ones. Experiments conducted on a dermatology image benchmark demonstrate the superiority of our proposed approach over several standard baseline methods, achieving significant performance improvements. Interestingly, we show through feature visualization and analysis that our method leads to context based lesion assessment that can reach an expert dermatologist level. © 2020 Institute of Physics and Engineering in Medicine.